ProMIL: Probabilistic Multiple Instance Learning for Medical Imaging
Fuente:
arXiv
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| Autores principales: | , , , , , |
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| Formato: | Preprint |
| Publicado: |
2023
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| Materias: | |
| Acceso en línea: | |
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| _version_ | 1866909134336557056 |
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| author | Struski, Łukasz Rymarczyk, Dawid Lewicki, Arkadiusz Sabiniewicz, Robert Tabor, Jacek Zieliński, Bartosz |
| author_facet | Struski, Łukasz Rymarczyk, Dawid Lewicki, Arkadiusz Sabiniewicz, Robert Tabor, Jacek Zieliński, Bartosz |
| contents | Multiple Instance Learning (MIL) is a weakly-supervised problem in which one label is assigned to the whole bag of instances. An important class of MIL models is instance-based, where we first classify instances and then aggregate those predictions to obtain a bag label. The most common MIL model is when we consider a bag as positive if at least one of its instances has a positive label. However, this reasoning does not hold in many real-life scenarios, where the positive bag label is often a consequence of a certain percentage of positive instances. To address this issue, we introduce a dedicated instance-based method called ProMIL, based on deep neural networks and Bernstein polynomial estimation. An important advantage of ProMIL is that it can automatically detect the optimal percentage level for decision-making. We show that ProMIL outperforms standard instance-based MIL in real-world medical applications. We make the code available. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2306_10535 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | ProMIL: Probabilistic Multiple Instance Learning for Medical Imaging Struski, Łukasz Rymarczyk, Dawid Lewicki, Arkadiusz Sabiniewicz, Robert Tabor, Jacek Zieliński, Bartosz Image and Video Processing Computer Vision and Pattern Recognition Machine Learning Multiple Instance Learning (MIL) is a weakly-supervised problem in which one label is assigned to the whole bag of instances. An important class of MIL models is instance-based, where we first classify instances and then aggregate those predictions to obtain a bag label. The most common MIL model is when we consider a bag as positive if at least one of its instances has a positive label. However, this reasoning does not hold in many real-life scenarios, where the positive bag label is often a consequence of a certain percentage of positive instances. To address this issue, we introduce a dedicated instance-based method called ProMIL, based on deep neural networks and Bernstein polynomial estimation. An important advantage of ProMIL is that it can automatically detect the optimal percentage level for decision-making. We show that ProMIL outperforms standard instance-based MIL in real-world medical applications. We make the code available. |
| title | ProMIL: Probabilistic Multiple Instance Learning for Medical Imaging |
| topic | Image and Video Processing Computer Vision and Pattern Recognition Machine Learning |
| url | https://arxiv.org/abs/2306.10535 |